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Record W176235279

An analysis of the Corporate Manslaughter and Corporate Homicide Act (2007): A Badly Flawed Reform?

2012· dissertation· en· W176235279 on OpenAlexfundaboutno aff
Benjamin Haigh

Bibliographic record

VenueDurham e-Theses (Durham University) · 2012
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsnot available
FundersUniversity of Cape TownUniversity of TorontoNewcastle University
KeywordsDoctrineCorporate liabilityConvictionCorporate lawCorporationPolitical scienceLawParliamentLegislationHomicideLaw and economicsCorporate governanceLiabilitySociologyPoliticsEconomicsManagementPoison control
DOInot available

Abstract

fetched live from OpenAlex

A conviction of a large corporation for manslaughter was in practice impossible. This statement was accurate when the prosecution utilised the identification/ “directing mind and will” doctrine. The position in relation to prosecutions against small companies was somewhat different. It was relatively straight-forward to successfully prosecute a “one-man band” style company due to its simple corporate structure. The Corporate Manslaughter and Corporate Homicide Act (2007) was enacted to resolve this issue. This thesis will endeavour to consider the lengthy process of law reform that ultimately resulted in the enactment of the legislation. It was the desire of Parliament that this Act would eliminate the difficulties that were faced by the courts when dealing with large complex corporate structures. This thesis will consider whether Parliament’s desire has been achieved or whether the same problems associated with the old doctrine still exist. This thesis will argue that the Corporate Manslaughter and Corporate Homicide Act (2007) has simply provided a gloss upon the identification doctrine and that we now have an “identification-plus” doctrine in the form of the “senior management test”. It is therefore questionable whether the new test would be any more effective when tested against a large corporate structure, than the old doctrine. In addition, this thesis will consider the Canadian model and whether any lessons can be learned from their approach to corporate criminal liability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.754
Threshold uncertainty score0.494

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0070.008
Scholarly communication0.0090.004
Open science0.0030.001
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.228
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2012
Admission routes2
Has abstractyes

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